Spatial relationships exist in our daily communication and provide many locality clues, but minimal attention has been devoted to positioning localities indoors with spatial relationships extracted from locality descriptions. Locality description generally contains spatial relationships (i.e., topological, distance, and direction relations) and reference objects (ROs). For locality description positioning, distance and direction relation convey more clue than topology and combined together can provide much clue to positioning. Explicit and implicit features are inherent characteristics of locality descriptions. Based on the analysis of locality descriptions, a classification scheme of indoor locality descriptions is provided. To address the positioning locality with spatial relationships, we propose a two-state method. First, the located region is obtained according to the fuzzy region of spatial relations in locality description. Second, the probability distribution is determined, which is a joint probability function that consists of relative direction and distance (quantitative or qualitative distance) membership functions. The relative direction membership function is conducted based on human’s direction cognition is related to angular information. The trapezoid quantitative distance membership function is based on indoor cognitive experiment. The qualitative distance membership function for indoor ROs is based on minimum Euclidean distance and stolen area. From the cognition and computation perspectives, the visible boundary met some restrictions is provided and a sampling method is defined. To obtain the unique positioning locality, a principle is proposed. We evaluate our method by conducting an indoor cognitive experiment and demonstrate that a positioning accuracy of 3.50 m can be achieved with semantically derived spatial relationships.